Proteins for Every Occasion

By Marcus Woo

Scientists are now able to design, in principle, almost any protein they want—a feat that was inconceivable just a few years ago. They are reengineering existing proteins found in nature, as well as constructing proteins from the ground up, atom by atom.

Custom-designed proteins could mean new and better vaccines, drugs, and other therapeutics; precisely designed biosensors; and catalysts capable of producing chemicals and pharmaceuticals in a more environmentally friendly manner.

Designed proteins may help solve many of the world’s biggest problems, says David Baker, PhD. As a professor of biochemistry at the University of Washington in Seattle, he’s been a pioneer in developing computational methods to build proteins from scratch, a process called de novo protein design.

Thanks to an improved understanding of how proteins fold, as well as advances in computing and genomic technology, experts say the field is now at an inflection point, with progress developing faster than ever.

“I’d like to think we can do most everything,” says William DeGrado, PhD, professor of pharmaceutical chemistry at the University of California, San Francisco. “And I’d like to think we can do a lot more than nature can do.”

Designing a Protein

Scientists have understood the basic principles behind protein folding since the 1960s: Electrostatic forces between and among the amino acids in a protein sequence pinch the chain, folding it into its lowest energy state—a flexible 3-D structure that changes in response to other nearby molecules. Since then, progress toward understanding how proteins reach their 3-D structures has been steady, including the first de novo computational design of a protein nearly 20 years ago and many other protein design successes since.

These days, researchers can fully model and create proteins from scratch using an advanced software package called Rosetta, developed by Baker’s lab. Rosetta users start with a desired protein structure and allow the program to fill in the details. Specifically, users first define a desired backbone shape—the arrangement of alternating amino and carboxyl groups that are part of each amino acid and that link together to form a polypeptide chain. The computer then calculates how well various side chains (which differ for each amino acid) fit around that backbone to produce the desired structure. “If you put a side chain in one position, that can dictate what’s on the neighboring position so they nestle together,” says Brian Kuhlman, PhD, professor of biochemistry and biophysics at the University of North Carolina at Chapel Hill. Meanwhile, Rosetta ensures that the whole molecule is at its lowest, most stable, energy state.

Yet most of the sequences that Rosetta comes up with for a particular structure won’t actually fold into a stable shape in the lab. And calculating backward to check whether the sequences do indeed generate the desired protein structure only gets you so far. To truly validate a sequence, one must synthesize the protein and test its stability. Until the advent of large-scale de novo approaches (see below), this required that proteins be designed and tested one at a time.

Neanderthal Design: Tweaking Natural Proteins

Most protein engineering to date has involved tweaking proteins found in nature to give them slightly different functions. Baker calls this Neanderthal protein design, similar to the strategy our primitive cousins would have employed—fashioning tools out of what was already lying around—for example, chipping away at a rock or sharpening a stick.

Baker’s team published an exciting example of this strategy in Nature Biotechnology in June 2017: They designed a protein that prevents mice from getting the flu. They knew that the flu virus’s surface contains a mushroom-shaped protein called hemagglutinin that enables the virus to infect cells by binding to a sugar molecule in the cell membrane. So they created a protein, dubbed flu glue, that can glom onto hemagglutinin, blocking it from infecting cells. It might not become medicine for humans anytime soon, but could be used to develop a quick and easy way to diagnose the illness.

A few years earlier, in 2011, Ingrid Swanson Pultz, PhD, translational investigator at the University of Washington, led the development of an enzyme called KumaMax that breaks down gluten. Since then, the molecule’s design has gone through further refinements to make it more effective. Pultz co-founded PVP Biologics, for which Baker serves as a scientific advisor, to further commercialize KumaMax in pill form. It might allow those with celiac disease to eat all the bread they want.

Kuhlman has been collaborating with the pharmaceutical company Eli Lilly to develop antibodies that can bind to two antigens at the same time, called bispecific antibodies. These kinds of antibodies can, for example, bind to both a tumor cell and an immune cell, thereby recruiting the body’s immune system to help fight cancer. The trick is making sure they don’t bind to other things in undesirable ways. In a 2016 paper published in Structure, Kuhlman’s lab developed a strategy for predicting the specificity of bispecific antibodies.

By designing proteins that bind to specific molecules, researchers can also make new types of biosensors. In work published in eLife in 2017, for example, Baker’s lab designed one that can signal the detection of the painkiller fentanyl. To test the sensor, the researchers incorporated it into a plant so the leaves turn color when it detects the molecule in question. This could ultimately lead to plants that can sense dangerous compounds.

In the future, Baker also wants to design proteins that can function like a rudimentary computer that does basic logic operations. This could lead to smart therapeutics such as designer proteins that can bind to a cell, determine whether it’s healthy or sick, and release or not release a drug.

De Novo Design of Simple Proteins

Neanderthal design has its limits: When you start with protein backbones that were created through the evolutionary process, you miss out on a huge variety of options that nature never tried. By contrast, de novo design can explore the entire realm of possible protein backbones, some of which might have greater potential to prevent or treat disease than natural (or Neanderthal-designed) molecules such as antibodies or antibiotics.

For the most part, de novo efforts have been restricted to simpler proteins because more complex structures are beyond current computational capabilities. And researchers are still far from being able to design proteins with the same sophisticated functions as those in nature. “The gap between what nature can do and what we can build is still very wide,” says Possu Huang, PhD, assistant professor of bioengineering at Stanford University.

But simpler proteins can still be useful. Huang, for example, has designed a donut-shaped protein called a TIM barrel, which has potential as a biosensor and as a building block to construct larger molecules. And Baker’s group has designed smaller proteins that can fit together to form so-called nanocages, which can serve as containers to deliver drugs.

Large Scale De Novo Design

The process of generating de novo protein designs took a huge leap in 2017 with a pair of papers (one in Science and one in Nature) out of the Baker lab. They are noteworthy because they signal a new era of large-scale, data-driven de novo design.

In the July 2017 Science paper, the Baker group used Rosetta to design 15,000 mini-proteins. They then tested these proteins for stability using a novel high-throughput experimental approach. They engineered yeast cells that could encode the test proteins and ferry them to the cells’ outer surfaces; and they bathed these cells in protease, an enzyme that breaks down proteins, to measure how the proteins reacted—to determine if they folded into a stable shape. At first, very few did. But the team analyzed the winners and losers to discover new rules of protein folding and incorporate winning features into the Rosetta pipeline. By the time they were done, their success rate for designing stable proteins had risen from 6 to 47 percent, and they had designed 2,788 novel proteins.

Such large-scale methods enable researchers to more efficiently design the proteins they want. “It gives you a lot of shots on goal,” DeGrado says. But the effectiveness of the feedback loop is equally important and suggests that machine learning could be used to better harness the lessons learned from large-scale testing—an approach Baker is now pursuing.

In the October 2017 Nature paper, the Baker team added a new application to the large-scale strategy: drug design. They used de novo techniques to design and test 20,000 mini-protein drug candidates for targeting viruses (such as flu) or toxins. When tested, their designs for flu successfully protected mice from infection. Unlike the Neanderthal-designed flu glue, these designs bind on the side of the hemagluttenin protein to block the virus from fusing with a cell. Moreover, the mini-binders are small, easy to make, more stable than antibodies, and don’t elicit much of an immune response (at least compared with modified natural proteins), suggesting that they may have potential as an anti-flu therapeutic.

Baker is optimistic. Neanderthal design is indeed useful today. But de novo design is still likely the future, he says. His lab has spawned a community of hundreds of protein designers, including Huang and Kuhlman. “There’s just a lot of energy and momentum in this area now, with lots and lots of smart young people going into it,” Baker says. “The future is very bright.”